code: def arg_parse():
      parser = argparse.ArgumentParser(
          description="Link pred arguments."
      )
      parser.add_argument(
          "--device", type=str, help="CPU / GPU device."
      )
      parser.add_argument(
          "--epochs", type=int, help="Number of epochs to train."
      )
      parser.add_argument(
          "--mode",
          type=str,
          help="Link prediction mode. Disjoint or all.",
      )
      parser.add_argument("--model", type=str, help="GCN or Spline.")
      parser.add_argument(
          "--edge_message_ratio",
          type=float,
          help="Ratio of edges used for message-passing (only in disjoint mode).",
      )
      parser.add_argument(
          "--multigraph",
          action="store_true",
          help="Example of multi-graph link pred (by copying CORA 10 times).",
      )
      parser.add_argument(
          "--batch_size",
          type=int,
          help="Batch size for multi-graph link pred. Always 1 for single-graph case.",
      )
      parser.add_argument(
          "--hidden_dim", type=int, help="Hidden dimension of GNN."
      )
  
      parser.set_defaults(
          device="cuda:0",
          epochs=500,
          mode="all",
          model="GCN",
          edge_message_ratio=0.6,
          multigraph=False,
          batch_size=1,
          hidden_dim=16,
      )
      return parser.parse_args()
  
imports: import sys
  import argparse
  from converter import *
  import copy
  import math
  import time
  import numpy
  from sklearn.metrics import *
  import torch
  import torch.nn.functional
  from torch.utils.data import DataLoader
  from torch_geometric.datasets import Planetoid
  from torch_geometric.datasets import TUDataset
  import torch_geometric.transforms
  import torch_geometric.nn
  from deepsnap.graph import Graph
  from deepsnap.dataset import GraphDataset
  from deepsnap.batch import Batch
  import pdb
label_desc: Use image node_id 1 for calling a global function with example usage: arg_parse() and returns: parser